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Record W1984073823 · doi:10.1088/0960-1317/22/3/035018

Lorentz-force transduction for RF micromechanical filters

2012· article· en· W1984073823 on OpenAlexaff
Sepehr Forouzanfar, Raafat R. Mansour, Eihab Abdel‐Rahman

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLorentz forceTransduction (biophysics)ResonatorLorentz transformationMicroelectromechanical systemsVibrationMaterials sciencePhysicsMagnetic fieldAcousticsOptoelectronicsClassical mechanicsChemistry

Abstract

fetched live from OpenAlex

This paper reports experimental results on the use of Lorentz-force transduction for RF micromechanical filters. The use of Lorentz-force transduction involves a significantly lower motional resistance compared to electrostatic methods. Clamped–clamped microbeams fabricated in CMOS35, PolyMUMPs and UW-MEMS are driven electrodynamically in the presence of an in-plane magnetic field. The out-of-plane vibrations of these microresonators are measured by the laser vibrometer to characterize their transduction performance. These measurements result in the identification of mode shapes and resonance specifications of the microresonators and provide data for motional resistance computations. These results that are confirmed by electrical measurements show significantly low values of motional resistance of the electrodynamically driven microresonators. Compared to electrostatic transduction, the computed motional resistance associated with Lorentz-force transduction is multiple orders of magnitude lower. Furthermore, Lorentz-force driving of clamped–clamped micromechanical resonators to higher modes up to ninth mode is demonstrated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.204
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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